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In a variety of domains, the United Nations (UN) asks countries and other actors to submit large volumes of documents. Natural Language Processing, especially topic modelling, can be used to gain insights into such datasets and their underlying politics. Here, we use topic models to analyse three different UN processes: the Global Stocktake under the Paris Agreement, the Food Systems Summit and the Water Action Agenda. We find marked similarities, despite the differing domains and underlying politics: submissions broadly represent the interests of the submitting party; moreover, many submissions discuss fairly general topics, while regionally dominant- and politically sensitive topics are either left out of the submissions (e.g. dietary change) or left out of official summaries (the Global Stocktake). We discuss the consequences for transparency efforts at the international level and share practical lessons on the topic modelling of multilingual datasets. BERTopic, in particular, can make use of recent advances in Large Language Models to create multilingual topic models without the need for translation. At present, however, it is also a more complex model to understand and fine-tune, so we discuss when it is appropriate to use such methods over more established alternatives, such as non-negative matrix factorization (NMF) and Latent Dirichlet Allocation (LDA). Overall, we stress that multilingual data sources can and should be considered more to address structural data inequalities in the tracking of climate impacts and adaptation, especially given the continuing rapid advances in multilingual NLP.